Ultrasonic diagnosis allows to display in real time how the heart beats or the fetus moves, by simply bringing an ultrasonic probe into contact with the body surface. This technique is highly safe, and hence allows repetitive examination. Furthermore, this system is smaller in size than other diagnosis apparatuses such as X-ray, CT, and MRI apparatuses and can be moved to the bedside to be easily and conveniently used for examination. In addition, ultrasonic diagnosis is free from the influences of exposure using X-rays and the like, and hence can be used in obstetric treatment, treatment at home, and the like.
Recently, there has been disclosed a technique for evaluating organs such as the liver by using such an ultrasonic diagnosis apparatus. This technique indicates the irregularity degree of the surface of the liver in the following manner: extracting a contour line by operation including smoothing and dividing the area of the portion sandwiched between the contour line and its approximate curve by the length of the approximate curve to obtain a normalized value based on the difference between the contour line and the approximate curve, thereby representing an irregularity. The technique colors the sandwiched portion and displays it on an image.
As a method of extracting the contour of an organ or the like by using an ultrasonic image or the like, for example, a method called the Fast Marching method is available. This method extracts an organ boundary from a tomogram by designating an initial contour inside the organ region and making ultrasonic waves continuously propagate from the initial contour in all directions. When making ultrasonic waves propagate, the method determines the path costs between pixels, that is, arrival time differences, according to local propagation velocities. Propagation velocities are obtained by a given expression such as the Eikonal equation dependent on the density values on an image. For example, the density values inside an organ are more uniform than those in a neighboring region of an organ boundary, and propagation velocities inside the organ are higher. That is, a contour propagates more quickly inside the organ. Separating a point at which the contour arrives from a point at which the contour does not arrive can quickly implement region segmentation.
As another method of extracting the contour of an organ or the like, the kernel method is available. According to this method, in data analysis, in order to grasp a nonlinear data structure, the original data is transformed into a form allowing easy analysis (a linear form in general) by applying nonlinear transformation to the original data.
The following problems, however, arise in the conventional method of evaluating organs in ultrasonic diagnosis.
That is, the conventional evaluation method executes smoothing in the process of extracting a contour line to remove noise near a boundary line. At this time, partial fine boundary irregularity information is lost together with noise. This partial fine boundary irregularity information includes a nodular pattern for determining the type of disease, and hence it may not be possible to make a satisfactory evaluation.
In addition, the conventional evaluation method evaluates a liver function as an irregularity based on the area of the portion sandwiched between a contour line and its approximate curve, that is, the variance relative to the approximate curve. However, the types and stages of liver disease vary, and there is not necessarily a one-to-one functional relationship between variances relative to approximate curves and liver functions. For this reason, simply using a variance relative to an approximate curve as an index for the diagnosis of a liver function may lead to an unsatisfactory result. More specifically, for example, as shown FIGS. 10A and 10B, with regard to contour lines C1 and C2 of two livers, approximate curves L1 and L2 are obtained, which extend across the respective curves. In this case, the variance of the contour line C1 relative to the approximate curve L1 is almost the same as that of the contour line C2 relative to the approximate curve L2. However, the comparison between the contour line C1 and the contour line C2 shows that their irregularity forms (for example, the degrees of zigzag) greatly differ from each other. Likewise, boundary lines of type C liver cirrhosis and type B liver cirrhosis in different stages may have the same variance.